Jason D. Lee

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	总计	2018 年至今
引用	14748	13465
h 指数	55	54
i10 指数	97	96

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of Washington在 cs.washington.edu 的电子邮件经过验证
Yuekai SunUniversity of Michigan在 umich.edu 的电子邮件经过验证
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley在 cs.berkeley.edu 的电子邮件经过验证
Nathan SrebroProfessor, TTIC and University of Chicago在 ttic.edu 的电子邮件经过验证
Tengyu MAStanford University在 stanford.edu 的电子邮件经过验证
Sham M KakadeHarvard University在 seas.harvard.edu 的电子邮件经过验证
Qi LeiAssistant Professor of Mathematics and Data Science, New York University在 nyu.edu 的电子邮件经过验证
Jonathan TaylorProfessor of Statistics, Stanford University在 stanford.edu 的电子邮件经过验证
Suriya GunasekarMSR Redmond在 ttic.edu 的电子邮件经过验证
Daniel SoudryAssociate Professor在 technion.ac.il 的电子邮件经过验证
Qiang LiuAssistant Professor of Computer Science, UT Austin在 cs.utexas.edu 的电子邮件经过验证
Meisam RazaviyaynUniversity of Southern California在 usc.edu 的电子邮件经过验证
Haochuan LiMIT EECS在 mit.edu 的电子邮件经过验证
Rong GeDuke University在 cs.duke.edu 的电子邮件经过验证
Benjamin RechtProfessor of EECS, University of California, Berkeley在 eecs.berkeley.edu 的电子邮件经过验证
Liwei WangProfessor, Peking University在 cis.pku.edu.cn 的电子邮件经过验证
Gaurav MahajanYale University在 eng.ucsd.edu 的电子邮件经过验证
Trevor HastieProfessor of Statistics, Stanford University在 stanford.edu 的电子邮件经过验证
Baihe HuangUniversity of California, Berkeley在 berkeley.edu 的电子邮件经过验证
MAX SIMCHOWITZMIT在 mit.edu 的电子邮件经过验证

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Jason D. Lee

其他姓名Jason Lee

Associate Professor of Electrical Engineering and Computer Science, Princeton University

在 princeton.edu 的电子邮件经过验证 - 首页

Machine Learning Theory Machine Learning Artificial Intelligence Statistics Optimization


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Gradient descent finds global minima of deep neural networks SS Du, JD Lee, H Li, L Wang, X Zhai arXiv preprint arXiv:1811.03804, 2018	1137	2018
Exact post-selection inference, with application to the lasso JD Lee, DL Sun, Y Sun, JE Taylor The Annals of Statistics 44 (3), 907-927, 2016	877	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	816*	2016
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	679	2016
On the theory of policy gradient methods: Optimality, approximation, and distribution shift A Agarwal, SM Kakade, JD Lee, G Mahajan Journal of Machine Learning Research 22 (98), 1-76, 2021	583*	2021
Matrix completion and low-rank SVD via fast alternating least squares T Hastie, R Mazumder, J Lee, R Zadeh Journal of Machine Learning Research, 2014	554	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	463	2016
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks M Soltanolkotabi, A Javanmard, JD Lee IEEE Transactions on Information Theory 65 (2), 742-769, 2018	435	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	423*	2014
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2018	386	2018
Implicit bias of gradient descent on linear convolutional networks S Gunasekar, JD Lee, D Soudry, N Srebro Advances in neural information processing systems 31, 2018	380	2018
Characterizing implicit bias in terms of optimization geometry S Gunasekar, J Lee, D Soudry, N Srebro International Conference on Machine Learning, 1832-1841, 2018	373	2018
Solving a class of non-convex min-max games using iterative first order methods M Nouiehed, M Sanjabi, T Huang, JD Lee, M Razaviyayn Advances in Neural Information Processing Systems 32, 2019	319*	2019
First-order methods almost always avoid strict saddle points JD Lee, I Panageas, G Piliouras, M Simchowitz, MI Jordan, B Recht Mathematical programming 176 (1-2), 311-337, 2019	317	2019
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	284	2017
Kernel and rich regimes in overparametrized models B Woodworth, S Gunasekar, JD Lee, E Moroshko, P Savarese, I Golan, ... Conference on Learning Theory, 3635-3673, 2020	279	2020
Gradient descent can take exponential time to escape saddle points SS Du, C Jin, JD Lee, MI Jordan, A Singh, B Poczos Advances in neural information processing systems 30, 2017	262	2017
On the power of over-parametrization in neural networks with quadratic activation S Du, J Lee International conference on machine learning, 1329-1338, 2018	259	2018
Learning the structure of mixed graphical models JD Lee, TJ Hastie Journal of Computational and Graphical Statistics 24 (1), 230-253, 2015	233*	2015
Gradient descent learns one-hidden-layer cnn: Don’t be afraid of spurious local minima S Du, J Lee, Y Tian, A Singh, B Poczos International Conference on Machine Learning, 1339-1348, 2018	230	2018

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